Results 91 to 100 of about 2,126,246 (333)

Visualizing and Understanding Curriculum Learning for Long Short-Term Memory Networks [PDF]

open access: yesarXiv, 2016
Curriculum Learning emphasizes the order of training instances in a computational learning setup. The core hypothesis is that simpler instances should be learned early as building blocks to learn more complex ones. Despite its usefulness, it is still unknown how exactly the internal representation of models are affected by curriculum learning.
arxiv  

Idiopathic Pulmonary Fibrosis Caused by Damaged Mitochondria and Imbalanced Protein Homeostasis in Alveolar Epithelial Type II Cell

open access: yesAdvanced Biology, EarlyView.
By systematizing a large body of evidence and propose a cascade relationship between protein homeostasis, endoplasmic reticulum stress, mitochondrial dysfunction, and pro‐fibrotic factor, providing a theoretical basis for ATII cells dysfunction as a possible pathophysiological initiating event for idiopathic pulmonary fibrosis.
Zhaoxiong Dong   +6 more
wiley   +1 more source

Lab Based Curriculum for CIS and Related Technology [PDF]

open access: yesarXiv, 2018
The Computer Information System (CIS) is information and communication technology in support of business processes. In this paper, we present a typical undergraduate computer information system curriculum examining the degree of lab intensity and its effect on the course efficacy.
arxiv  

Data Distribution-based Curriculum Learning [PDF]

open access: yesIEEE Access, vol. 12, (2024), pp. 138429-138440
The order of training samples can have a significant impact on the performance of a classifier. Curriculum learning is a method of ordering training samples from easy to hard. This paper proposes the novel idea of a curriculum learning approach called Data Distribution-based Curriculum Learning (DDCL).
arxiv   +1 more source

Generation of Advanced Blood–Brain Barrier Spheroids Using Human‐Induced Pluripotent Stem Cell‐Derived Brain Capillary Endothelial‐Like Cells

open access: yesAdvanced Biology, EarlyView.
In this article, the establishment and characterization of a self‐assembled 3D BBB spheroid model using human induced pluripotent stem cell (hiPSC)‐derived and primary cells is reported. Spheroids demonstrate in‐vivo like tight junction ultrastructure and, in comparison to 2D mono‐cultures, higher transcript expression of BBB specific genes.
Sanjana Mathew‐Schmitt   +10 more
wiley   +1 more source

Funneling Versus Focusing: When Talk, Tasks, and Tools Work Together to Support Students’ Collective Sensemaking

open access: yesScience Education International, 2018
Rigorous and responsive science teaching is based on supporting all students in making progress in their understanding of important science ideas over time.
Sara Hagenah   +2 more
doaj   +1 more source

Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks [PDF]

open access: yesProceedings: 35th International Conference on Machine Learning (ICML), oral, Stockholm Sweden, July 2018, 2018
We provide theoretical investigation of curriculum learning in the context of stochastic gradient descent when optimizing the convex linear regression loss. We prove that the rate of convergence of an ideal curriculum learning method is monotonically increasing with the difficulty of the examples.
arxiv  

Characterization and Optimization of Vesicle Properties in bioPISA: from Size Distribution to Post‐Assembly Loading

open access: yesAdvanced Biology, EarlyView.
The paper explores the creation and characterization of vesicles through biocatalytic Polymerization‐Induced Self‐Assembly (bioPISA), focusing on achieving size uniformity using centrifugation techniques. It examines the effects of stirring speed on vesicle morphology and analyses the internal polymer‐rich structure using fluorescence correlation ...
Andrea Belluati   +7 more
wiley   +1 more source

Learning Curriculum Policies for Reinforcement Learning [PDF]

open access: yesProceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), 2018
Curriculum learning in reinforcement learning is a training methodology that seeks to speed up learning of a difficult target task, by first training on a series of simpler tasks and transferring the knowledge acquired to the target task. Automatically choosing a sequence of such tasks (i.e. a curriculum) is an open problem that has been the subject of
arxiv  

When Do Curricula Work? [PDF]

open access: yesarXiv, 2020
Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standard i.i.d. training.
arxiv  

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